Premier League Result Prediction AI

I need a reproducible machine-learning pipeline that can predict the outcome of English Premier League matches (home win, draw, away win). The model must be trained strictly on domestic league data and should blend three information streams: historical match results, individual player statistics, and overall team statistics.

You will handle end-to-end preparation: sourcing or scraping current and past EPL datasets, cleaning and merging them, engineering meaningful features (form, injuries, recent xG, etc.), and selecting the most suitable algorithm—whether that turns out to be an ensemble of gradient-boosting trees, an LSTM sequence model, or another proven approach in Python with scikit-learn, TensorFlow or PyTorch.

Once trained, validate the model with an appropriate hold-out or cross-validation strategy and report accuracy, precision/recall and F1 so we can benchmark improvements over time. I also want clear commentary on feature importance or SHAP values to understand what is driving the predictions.

Deliverables
• Fully commented Python source code or notebooks
• Cleaned, ready-to-use dataset (or documented links/scripts to recreate it)
• Trained model artefact and loading script
• A concise README explaining setup, training, and inference steps (CLI or lightweight REST endpoint)

If something in the data proves unavailable, flag it early so we can adjust. My priority is a transparent, well-documented workflow that I can extend each new season.